To learn richer feature representations of data, contrastive learning [1]}, [2]}, [3]} proposed augmenting the data during training, and contrasting a model’s representations of the data by pulling samples from the same samples closer and pushing samples from different samples further away. SimCLR [3]} adopted some augmentations including spatial distortion and chromatic distortion to enrich feature representations. Considering KL divergence ignores important structural knowledge of the teacher network in knowledge distillation, Tian  [5]} adopted contrastive learning to a student so that the student can capture significantly more information in the teacher’s representation of the data. Park  [6]} employed contrastive learning to improve unpaired image-to-image translation quality. Recently, Wang  [7]} introduced contrastive learning to improve the performance of the task of the image super-resolution. However, there are still few works to apply contrastive learning to MRI reconstruction. In this work, we aim to constrain the solution space by using the contrastive loss function, which could be achieved by pulling samples from the same samples closer and pushing samples from different samples further away.
